NPS
In May, SIAA announced preferred access to Momentum AMP’s agency management suite, at member pricing, for its 5,200+ member agencies through its TechFinder program. Momentum’s own SIAA page lists the price as a 10% discount, or 15% on plans over 10 seats. The offer gives members a vetted option at a lower cost. They still have to decide whether that convenience, and its terms, beat choosing their own technology stack. [Source]
Offers like it are why 2027 looks different.
An agency can experiment with AI on its own. Making AI work across its operations takes a different investment: connected systems, reliable data, clear rules, and someone accountable for the result. Larger organizations can spread that cost across many agencies. Smaller ones must fund it themselves, share it, or accept slower progress.
For an independent agency, the question is how to build the operating capacity to use AI well, and how much control to trade for help.
Why AI adoption stalls in independent insurance agencies
In InsuredMine’s AI Pulse Survey this year, 84% of customer agencies said they use AI. What they use it for tells you where the cost sits. 72% draft email and communications, and 56% summarize documents. Then use falls: 32% for data entry and workflow automation, 21% for automated client follow-up, 18% for chatbots and client intake, and 15% for predictive risk scoring.
The gap is between having access to AI and having an operation built to use it. Drafting an email asks little of the agency’s systems. Automating data entry needs access, clean records, and rules about what the AI may touch.
Who pays for AI in an insurance agency? Owners, networks, and alliances
Ownership is the first answer. A buyer can fund the work once and apply it across many agencies. American Growth Insurance has nearly $70 million in backing and spent the past year developing and testing its AI operating model with 10 agencies. It says the model improved their average profits by more than 50%. That is a company claim, but the incentive is plain. PwC expects distribution deal volume to moderate as buyers get more selective and integration capacity tightens. When integration capacity is scarce, an agency that arrives connected is cheaper to absorb, which matters to any owner who may sell one day. [Source]
Pooling is the second answer. A network can’t require anything of its members, but it can spread the cost of vetting and negotiating. SIAA, whose members write over $18 billion in premium, is one example. A 20-person agency can’t negotiate that alone. Thousands of agencies can. The owner’s question is what comes with the discount: which tools, and whose rules. [Source]
Each route moves the cost off the agency. None has produced a common AI standard yet, and nothing in the evidence says one is arriving on a schedule. But the economics point in one direction.
What private equity and agency networks mean for your AI strategy
For an owner weighing an offer, help raises three separate questions. Who pays for the work? Who decides which tools, workflows, and access rules apply? Who is accountable when something goes wrong with a client?
They don’t have to move together. An agency can share the cost of its technology and keep its own rules. Another can stay independent on paper and depend entirely on one platform. Payment is the easiest of the three to hand off. Decisions can move with it if the contract says so. Accountability stays with the agency, because the client relationship does.
AI readiness by agency size: from small to enterprise
How much of the work you can carry, and how much you’d hand off, shifts as the agency grows. Headcount is only a proxy: a 30-person agency with clean data can be readier than a 100-person agency with fragmented systems. But the main constraint tends to change in a recognizable order.
Small and growing: capability. At this size the hard part is knowing what to automate. When nobody owns the question, each person tries tools alone and the agency never learns as a whole. Outside vetting, from a network, a vendor, or a peer agency, earns its keep here. Hand off the evaluation. Keep the two things nobody can hold for you: a one-page AI policy and one person accountable for how AI is used.
Mid-market agencies: adoption. The problem shifts from finding tools to getting people to use them well. The risk is trust. A client who gets a message that doesn’t sound like their producer notices, and the relationship pays for it. Hand off the tooling. Keep the rules: when AI drafts, who reviews it, and when a client is told. The producer stays the sender.
Large agencies: consistency. More people means more ways to do the same job. In the Big “I” technology survey, about 45% of agencies reported using ChatGPT or other public models, while only 13% had a formal AI policy. Spread that gap across 150 people and you have a lot of unmanaged decisions about client data. You can hand off the heavy implementation. Keep the approved tool list and the shared playbooks, and standardize before you scale. [Source]
Enterprise agencies: control. Enterprises can fund more of the work internally, which turns the problem into coordination. More systems, more teams, and more places for AI to touch client data. The question stops being whether to use AI and becomes who can say no. Keep the access controls, the audit trail, and one executive with the authority to approve or reject AI tools across the organization.
What ready looks like
At any size, four foundations sit with the agency on any terms. They are a minimum diagnostic, not a certification:
- Embedded: at least one recurring workflow uses AI in normal operations, beyond individual experimentation.
- Connected: that workflow has authorized access to the agency data and systems it needs.
- Governed: written rules cover approved tools, permitted data, and human review.
- Accountable: one named person owns performance, exceptions, and corrective action.
An agency that meets all four can share the cost of its tools and still decide how they run. An agency that meets none can still accept help, but on terms it will have less power to shape.
The AI Pulse Report releases next week, with results from our survey of customer agencies.
In the meantime, if you’re looking to grow your agency, and you want AI adopted across the whole agency value chain, from capture and nurture through renewals and retention, with producers equipped to bring in more revenue, contact us
